We use cookies to personalize content and ads, to provide social media features and to analyze our traffic. We also share information about your use of our site with our social media, advertising and analytics partners. By continuing to browse our
site without changing your settings, you are agreeing to accept all cookies on the site.

Detection and Attribution of Organophosphate Pesticide Signatures

Challenge

Organophosphate pesticides (OPPs) are a highly toxic class of chemicals still used for agricultural purposes in many countries. Their high toxicity and wide availability could make them attractive to terrorists or criminals for use as chemical threat agents (CTAs).

The U.S. Department of Homeland Security came to Battelle for help in identifying new analytical and statistical methods for characterization of OPPs. Common analytical techniques such as gas chromatography coupled with nitrogen/phosphorus detection or mass spectral detection are able to accurately identify the parent materials in an unknown compound. However, they do not have the sensitivity needed for source attribution or forensic analysis. New methods were needed that would be able to detect trace impurities in order to distinguish characteristics such as the OPP source or manufacturing method and provide a distinct chemical “fingerprint” for use in forensic investigations.

Solution

Researchers at Battelle conducted a study to determine the chemical attribution signatures (CASs) for several commercially available OPPs. The compounds were analyzed using two-dimensional gas chromatography with time-of-flight mass spectrometric detection (GC×GC-TOFMS). Researchers then applied statistical pattern recognition techniques to the data collected in order to determine the unique chemical fingerprint of each sample. Replicate samples of chlorpyrifos, dichlorvos and dicrotophos were analyzed to identify CASs.

GC×GC-TOFMS provides much higher sensitivity than traditional analytical methods, giving it greater potential for forensic analysis. By using two dimensions of separation instead of one, it provides substantial increases in chromatographic separation, allowing for more detailed analysis. It also allows analysts to make tentative identification of unknown compounds in the absence of analytical standards. However, it can be difficult to sort through the large data sets produced by the analysis in order to find meaningful components or patterns.

Because of the large number of predictor variables produced, data could not be analyzed using traditional statistical methods. Battelle researchers evaluated three statistical pattern recognition methods—Random Forest, Lasso and Elastic Net—to determine which method was most effective in classifying the compounds.

Outcome

The research demonstrated that combining GC×GC-TOFMS analysis with statistical pattern recognition techniques is effective for classification and source attribution of OPPs. Out of the statistical methods tested, the Random Forest technique was the most effective, showing successful classification rates of up to 97-100% for each OPP compound tested. The results suggest that these methods could be used to trace OPPs to their likely sources for forensic investigations. The same methods could be used for chemical forensic analysis of other hazardous materials, contaminants or chemical weapons, or for analysis of other complex mixtures such as food products and additives.